Imposter Phenomenon: Impact on First Generation College Students
Bibliographic record
Abstract
Background:Current literature has evaluated the dynamic of IP amongst college graduates transitioning into entry-level jobs, professionals in their careers, and minority groups entering higher education. However, there is a gap in the current literature that overlooks the interaction between occupational experiences, IP, and FGCS. The purpose of this study is to use grounded theory to observe the experiences of IP among FGCS enrolled in four year universities.The research question developed to guide this study asks: What are the occupational experiences of FGCS who identify with the IP? Based on the gap between IP and FGCS, it is hypothesized that FGCS do encounter IP. Moreover, the way they encounter their occupations may be dissimilar to CGCS or to those who do not identify with IP. Methods: This qualitative study uses snowball and purposive sampling for an initial screening survey to collect demographics and perceptions of the Imposter Phenomenon through a sample narrative with prompted questions. Subsequent to this survey is a semi-structured interview reflecting principles from the Canadian Model of Occupational Performance and Engagement (CMOP-E). Thematic analysis will be used to code and theme commonalities within the collected data to generate a grounded theory based on the Imposter Phenomenon. Results & Conclusion: Data on this study is still being collected and themed; therefore, results and conclusion cannot be disseminated until all interviews are conducted and analyzed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".